AI in Financial Services: The Pros And Cons

The traditional playbook for credit brokers and investors is getting a major rewrite. Artificial intelligence (AI) is revolutionising the financial sector, forcing professionals to rapidly develop a comprehensive understanding of this burgeoning technology and navigate AI’s emerging market effectively.

As a credit broker, AI can streamline processes and enhance risk assessment, while investors can harness AI tools to perform market analysis and boost portfolio management capabilities.

This article explores the potential of AI in the finance industry, looking at its advantages for improving efficiency and risk mitigation, and its potential limitations, such as privacy concerns and output inaccuracies.

Read on to learn how credit brokers and investors can make informed decisions on leveraging AI in financial services responsible to achieve their financial goals.

What is Artificial Intelligence (AI)?

AI is a field of computer science that focuses on mimicking human cognitive functions such as identifying patterns in data. AI technologies can also be used to automate processes that would otherwise take employees hours to complete manually, such as data entry.

Generative AI (Gen AI) is a subset of AI that focuses on creating entirely new content from structured data (such as spreadsheets) and unstructured data (such as content within emails). Gen AI leverages Large Language Models (LLMs) which are essentially complex algorithms trained on massive data sets. LLMs allow Gen AI platforms like ChatGPT and Gemini to understand natural language queries and generate human-like responses.

The emergence of Gen AI (powered by LLMs) marks a pivotal development in information technology, as it enables anyone with the ability to write a simple prompt to perform complex computational tasks, such as analysing financial records or generating compliant loan agreements in seconds.

Use Cases for AI in Finance

The market for AI in financial services is growing rapidly, in 2023, its global market value was an estimated $1.19 billion, and it’s projected to reach £13.33 billion by 2033 (achieving a 27.29% CAGR). According to a recent survey from Sapio Research, AI is most commonly used in finance roles, with 63% of organisations utilising AI in finance across a range of use cases including:

• Automating repetitive tasks: AI algorithms can process vast amounts of data to automate repetitive tasks such as loan application processing and financial reporting. This can free up valuable time and resources, enabling brokers to focus on developing their deep knowledge of the industry and their customers’ needs.
• Risk mitigation: AI in finance technology can identify anomalies in financial data, improving companies’ fraud detection and risk assessment capabilities. AI-powered credit scoring tools, for example, can analyse a potential customers’ creditworthiness faster and more accurately than traditional methods, enabling companies to expand their customer base and minimise their exposure to risk.
  • A 2024 study by UK Finance reveals that 91% of UK financial institutions already deploy predictive analytics to enhance fraud detection and other back-office processes.
• Market analysis: AI algorithms can analyze third-party data from industry risk databases, financial news and social media sites to provide valuable insights for brokers and investors. This allows for a more comprehensive understanding of market forces, leading to more informed decision-making.
• Customer service: AI-powered chatbots can act like robo-advisors, providing fast and accurate answers to common customer queries. Moreover, Gen AI can also be used to provide personalised educational content and make sophisticated investment advice more accessible to a wider audience.

Pros of Using AI in the Finance Industry

Beyond basic automation, AI in financial services can empower brokers and investors with financial knowledge and personalised guidance. Let’s take a closer look at some of the technology’s benefits:

• Democratisation of financial data: AI platforms promote the democratisation of data across organisations. For instance, generative AI tools can summarise meeting notes, presentations and complex financial documents and transform the information into actionable insights teams can use to boost their collaboration and decision-making capabilities.
• Improve efficiencies and save money: AI-driven automation can speed up brokers’ workflows, potentially increasing their productivity by as much as 14% (according to research from MIT). This translates to more loans processed, happier clients and cost savings that can be reinvested into growing your business.
• Keep up with compliance requirements: As financial compliance regulations evolve, more and more companies will need AI-driven compliance tools. Sometimes known as RegTech, these tools can help financial services companies automate checks and keep up with changes in the law without needing to hire compliance experts or drastically alter their processes. This can help companies save time and resources and reduce their noncompliance risk.
• Improve innovation capabilities: AI can play a huge role in optimising financial processes, allowing teams to focus on developing innovative solutions and bringing them to market with minimal disruption to core business processes. For example, many AI platforms offer low-code or completely no-code tools, empowering finance teams from all technical backgrounds to develop effective data solutions without the help of coding experts. AI-driven analytics can also predict future outcomes, assess market trends and risks and help steer companies into making the right decisions when it comes to product development.

Potential Concerns with AI in the Financial Industry

AI in financial services offers significant advantages for brokers and investors. However, it’s crucial to acknowledge its potential drawbacks including:

• Data privacy concerns: LLMs rely on vast amounts of sensitive financial and personal data to function. This raised significant concerns about data privacy and security amongst 43% of recipients polled by Sapio Research. Therefore, brokers and investors must choose AI platforms with robust data security and access controls to ensure financial data is protected from misuse and theft.
• Output inaccuracies: Gen AI models can prioritise originality over factual correctness in some instances. This can lead to misleading or inaccurate outputs, which brings AI’s usefulness in financial contexts into question.As the finance industry relies on precise information for sound decision making, human oversight will always be needed to check AI outputs and ensure they benefit credit brokers, investors and clients.
• Lack of explainability: The complexity of LLMs make it difficult for users to understand how they arrive at their answers. This lack of transparency makes it challenging for brokers and investors to explain their financial decisions to clients and stakeholders. Luckily, some AI solutions mitigate these concerns by incorporating explainable AI (XAI) which demonstrates the reasoning behind AI outputs, fostering trust in financial decision-making.
• Ethical concerns: AI is only as reliable as the data it’s trained on. Unfortunately LLM training data is vast and some of it perpetuates biases which can lead to discriminatory lending practices or investment recommendations. Brokers need to be aware of potential biases and take active steps to ensure their data sources do not put their company or customer base at risk. There are various resources online that can help firms develop responsible AI practices to counteract unethical AI outputs.
• Job displacement: Change management is a huge concern for companies exploring potential AI use cases. AI in the financial industry, in particular, raises concerns about job security due to the high number of daily tasks that involve data analysis and report generation, two activities where automated technologies can excel. Recent research suggests that AI-powered automation can save employees 65 minutes a day (38 days per year) on repetitive tasks.
  • However, brokers and investment firms shouldn’t be worried about job loss, as professional expertise will still be vital for customers seeking the best solutions for their finances. In essence, the best way forward is to learn how to make the best use of AI in financial services to enhance your overall value proposition.

Looking to Transform Your Business? 365 finance Can Help

While AI offers exciting possibilities for brokers and investors, it’s no magic bullet. Businesses need a strong financial acumen and access to the latest technologies to minimise risk in their transformation strategies. Here at 365 finance we can be your trusted partners in securing alternative financial solutions for your clients.

We’re a recognised and accredited member of BMCAA, FSB and NACFB offering award-winning expertise in flexible funding solutions. For example, our innovative Rev&U product provides Merchant Cash Advances (MCAs) of £10,000 to £400,000 for qualifying businesses. Our expert team is here to help you connect your clients with the funding they need to spur their growth, and help them find  financial solutions to meet their diverse needs.

At 365 finance, we can provide both long and short-term financial solutions, with revenue-based funding available from £10,000 to £400,000 in capital. Apply for Rev&U today without affecting your credit score, or speak to our team to find out how we can help your business. To find out more, head to our website.